3
$\begingroup$

I have a variable whose values are summated scale scores constrained within the range 3 to 21, inclusive. I considered this variable as a scale variable (though with restricted range). In order to model the relation of this variable to a number of explanatory variables, I first wanted to know what the distribution of this variable is. For this purpose I used Cox’s dpplot command in Stata to fit various distributions to the variable. The plots are given below.

enter image description here

Based on the plot, what may I conclude about the distribution of the variable? It seems to be close to all distributions except to the exponential distribution.

Improve this question
$\endgroup$
4
  • $\begingroup$ Look like normal distribution except the exponential one $\endgroup$
    – sakarie adan
    Sep 17, 2014 at 13:02
  • $\begingroup$ Are the values of your variable always integers? $\endgroup$
    – Aniko
    Sep 17, 2014 at 15:32
  • $\begingroup$ Yes, integers between 3 and 21. $\endgroup$
    – Ayalew A.
    Sep 17, 2014 at 15:41
  • $\begingroup$ If your goal is to use as response in a regression model, see stats.stackexchange.com/questions/477565/… $\endgroup$
    – kjetil b halvorsen ♦
    Nov 3, 2021 at 11:31

2 Answers 2

Reset to default
4
$\begingroup$

Presumably by 'restricted range' you mean that there's both an upper and lower bound to the possible values the data can take.

Several of the distributions are reasonably consistent with the general shape of your data, but since your data are (i) discrete and (ii) bounded above, your data cannot actually come from any of those distributions.

As a way of choosing a distributional model, this activity strikes me as potentially a form of data dredging.

Note that if you're looking to use regression, then your immediate assumption is that you have not one distribution, but a different distribution (at least in respect of location) at each set of $x$ values.

As such, looking at the marginal distribution of $y$ (in terms of trying to identify a single distribution to describe it) is of little use - it doesn't relate to the regression assumptions, which involve the conditional distribution, not the marginal.

Even when the conditional distributions are not normal, you may still be able to use least squares - most forms of inference will still be okay in large samples, and even in small samples it may be okay as long as you adapt any inferential procedures. Of greater concern than distribution shape will be the assumption of linearity and homoskedasticity.

Improve this answer
$\endgroup$
2
  • $\begingroup$ For a variable with such distributions, may it be better to use regression techniques that do not require distributional assumptions (quantile regressions) to model its relation with explanatory variables? $\endgroup$
    – Ayalew A.
    Sep 18, 2014 at 5:23
  • 2
    $\begingroup$ Agreed. Paragraph #2 is especially important. $\endgroup$
    – Nick Cox
    Sep 21, 2014 at 22:45
3
$\begingroup$

Let $X$ denote your variable. Assuming that the scoring system is such that 3 is the smallest possible value that could be observed, $X-3$ has integer values between 0 and 18. If the theoretical range of values is different, than you might have to do a different adjustment (or no adjustment at all). I would explore overdispersed generalizations of the binomial distribution such as the beta-binomial distribution to model your data. Depending on what you want to do, it is also possible that you don't need a parametric distribution.

Improve this answer
$\endgroup$
5
  • $\begingroup$ Subtracting $3$ seems risky, since these plots suggest that with more samples even smaller values of $X$ ought to appear. What is the point of the subtraction anyway? Why not model $X$ directly as a Beta-Binomial? $\endgroup$
    – whuber ♦
    Sep 19, 2014 at 15:00
  • 1
    $\begingroup$ I assumed that the 3 to 21 range is the range of possible values, not the actual range that happened to be observed. But I will edit my answer to clarify this assumption. $\endgroup$
    – Aniko
    Sep 19, 2014 at 22:18
  • $\begingroup$ @whuber: The range of possible values is between 3 and 21, inclusive. Less than 3 is not possible. Neither is above 21. $\endgroup$
    – Ayalew A.
    Sep 21, 2014 at 9:15
  • 1
    $\begingroup$ Ayalew, that's an important fact to put into the question itself, for several reasons. First, although you have made a similar statement in the question, it is ambiguous because it could easily be read to mean that the values in the dataset range from 3 to 21 rather than that all values are constrained within this range. Second, all your graphics strongly suggest the possibility of values beyond this range, because they all include such values on their axes. Third, all the distributions you attempt to fit do not have constrained ranges. $\endgroup$
    – whuber ♦
    Sep 22, 2014 at 13:46
  • $\begingroup$ @whuber, now I have edited the question to minimize the ambiguity. $\endgroup$
    – Ayalew A.
    Sep 23, 2014 at 6:21

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.